ABSTRACT
Objective: Study the feasibility of using artificial intelligence as a sensitive and specific method for COVID-19 screening in patients with respiratory conditions, using chest CT scan images and a telemedicine platform. Methods: From March 2020 to June 2021, the authors conducted an observational descriptive multicenter feasibility study based on artificial intelligence (AI) for COVID-19 screening using chest images of patients with respiratory conditions who presented at public hospitals. The AI platform was used to diagnose chest CT scan images; this was then compared with molecular diagnosis (RT-PCR) to determine whether they matched and to analyze the feasibility of AI for screening patients with suspected COVID-19. A telemedicine platform was used to send images and diagnostic results. Results: Screening of 3 514 patients with a suspected COVID-19 diagnosis was performed in 14 hospitals around the country. Most patients were aged 27 to 59 years, followed by those over 60. The average age was 48.6 years; 52.8% were male. The most frequent findings were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, and diffuse ground glass opacity, among others. There was an average of 93% matching and 7% mismatching between images analyzed by AI and RT-PCR. Sensitivity and specificity of the AI system, obtained by comparing AI and RT-PCR screening results, were 93% and 80% respectively. Conclusions: The use of sensitive and specific AI for stratified rapid detection of COVID-19 in patients with respiratory conditions by using chest CT scan images and a telemedicine platform in public hospitals in Paraguay is feasible.
Objetivo: Examinar a viabilidade do uso de inteligência artificial como um método sensível e específico de triagem de COVID-19 em pacientes com afecções respiratórias, empregando imagens obtidas por exame de tomografia do tórax e uma plataforma de telemedicina. Métodos: Entre março de 2020 e junho de 2021, foi realizado um estudo observacional descritivo multicêntrico sobre a viabilidade do uso de inteligência artificial (IA) para a triagem de COVID-19, empregando imagens do tórax de pacientes com afecções respiratórias atendidos em hospitais da rede pública. O diagnóstico das imagens obtidas em tomografia do tórax foi realizado por meio de uma plataforma de IA e, em seguida, cotejado com o diagnóstico molecular (RT-PCR) para determinar a concordância entre os métodos utilizados e analisar a viabilidade deste processo para a triagem de pacientes com suspeita de COVID-19. As imagens e os resultados do exame diagnóstico foram disponibilizados em uma plataforma de telemedicina. Resultados: Foi realizada a triagem de 3 514 pacientes com suspeita de COVID-19 atendidos em 14 hospitais de todo o país. Os pacientes, na sua maioria, tinham entre 27 e 59 anos de idade ou pertenciam à faixa etária acima de 60 anos, com média de idade de 48,6 anos, sendo que 52,8% eram do sexo masculino. Os achados mais comuns foram pneumonia grave, pneumonia bilateral com derrame pleural, enfisema pulmonar bilateral e opacidade difusa em vidro fosco, entre outros. Verificou-se, em média, 93% de concordância e 7% de discordância entre as imagens analisadas com uso de IA e os resultados do exame de RT-PCR, com uma sensibilidade de 93% e especificidade de 80% desse sistema de triagem. Conclusões: Demonstrou-se que o uso de um sistema de IA sensível e específico é viável nos hospitais públicos do Paraguai para a detecção rápida estratificada de COVID-19 em pacientes com afecções respiratórias, empregando imagens de exame de tomografia do tórax e uma plataforma de telemedicina.
ABSTRACT
IntroductionThe diagnosis and management of chronic diseases during the coronavirus disease 2019 (COVID-19) pandemic was one of the biggest challenges facing healthcare systems globally, especially in low-income countries. Since basic health care for chronic diseases can overwhelm the capacity of conventional face-to-face healthcare services, there is growing interest in using information and communication technology and telemedicine to improve access to medical services that are often not consistently available in rural communities. In this context, telemedicine tools should be directed toward maintaining basic health services for patients with chronic conditions in rural and underserved hospitals. This study evaluated a telemedicine system in remote public hospitals in Paraguay to demonstrate how telemedicine improved access to tertiary level diagnostic services for patients with chronic conditions.MethodsThis descriptive study evaluated the use of telemedicine for diagnosing patients in remote public hospitals to improve provision of basic health services to patients with chronic disease during the COVID-19 pandemic. The type and frequency of diagnostic studies performed were determined.ResultsDuring the study 677,023 telediagnoses were performed in 67 hospitals. The 435,568 electrocardiograms performed in 61 hospitals indicated normal physiology (60.1%), unspecified arrhythmias (10.5%), and sinus bradycardia (8.4%). The 227,360 teletomography tests performed in 12 hospitals were undertaken on the head (52.4%) because of trauma (motorcycle accidents) and cerebrovascular diseases, chest (15.8 %), and other anatomical regions. The 14,076 electroencephalograms performed in 19 hospitals were undertaken for antecedents of seizure (53.3%), disease progression controls (14.0%), and headache (12.5%). Nineteen prenatal ultrasound scans were conducted.ConclusionsAlthough the results are promising for using telemedicine to bridge gaps and improve equity in the provision of basic health services for patients with chronic diseases in remote locations during the COVID-19 pandemic, a widespread use assessment should be undertaken before this tool is adopted.
ABSTRACT
IntroductionThe evolution of advances in informatics, technology in medicine, and artificial intelligence (AI) offers opportunities to enhance health care during the coronavirus disease 2019 (COVID-19) pandemic. The challenge for biomedical engineers is to implement these developments in clinical practice to improve global health. Populations living in low-income countries do not have access to specialist care and quality diagnostic services for COVID-19. Therefore, an AI system based on a telemedicine platform for diagnosing COVID-19 could help mitigate the lack of highly trained radiologists at regional hospitals and serve as a triage system for rationalizing the use of reverse transcription polymerase chain reaction (RT-PCR) testing and other health resources in low-income countries. Thus, the utility of an AI system for diagnosing COVID-19 in Paraguay was investigated.MethodsThis is a descriptive multicenter observational feasibility study of an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties who attended public hospitals across the country.ResultsBetween March 2020 and August 2021, 3,514 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of the patients was 48.6 years (52.8% were men);the most common age ranges were 27 to 59 years, followed by older than 60 years and 19 to 26 years. The most frequent findings on the CT images were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes. Overall, there was 93 percent agreement and 7 percent discordance between the AI system and the RT-PCR test results. Compared with RT-PCR testing, the AI system had a sensitivity of 93 percent and a specificity of 80 percent.ConclusionsParaguay has an AI-based telemedicine screening system for the rapid detection of COVID-19 that uses chest CT images of patients with respiratory conditions.
ABSTRACT
Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.
ABSTRACT
IntroductionHealth care for patients with chronic pathologies was scarce and limited worldwide during the COVID-19 pandemic. The challenge for clinical and biomedical engineers is to develop a bridging system to maintain the basic health services for chronic pathologies. Populations living in low-income countries did not have access to basic health services during the pandemic and depended on the scarce resources of their emergency health system. There were also equity issues between urban and rural populations. In this context, telemedicine tools should be directed toward maintaining the basic health services for patients with chronic pathologies. This study evaluated the results of a telemedicine system in remote public hospitals in Paraguay to show how health care for patients with chronic pathologies has been maintained by providing access to tertiary level diagnostic services by specialists.MethodsThis descriptive study evaluated the results of using telemedicine between 2014 and 2020 for diagnosis in remote public hospitals to bridge the gap in providing basic health services for patients with chronic pathologies during the COVID-19 pandemic.ResultsA total of 620,289 telediagnoses were performed in 67 hospitals. The 399,806 electrocardiogram diagnoses performed in 61 hospitals were normal (62%) or showed unspecified arrhythmias (13%) and sinus bradycardia (10%). The 207,597 teletomography tests performed in 12 hospitals were performed on the head because of motorcycle accidents and cerebrovascular diseases (54%), on the chest (14%), and other anatomical regions. The 12,867 electroencephalograms performed in 19 hospitals were for the antecedents of seizure (54%), evolutionary controls (14%), and headache (12%). The 19 ultrasound studies corresponded to prenatal controls.ConclusionsAlthough the telemedicine tool implemented in public health to bridge the gap in basic health services for patients with chronic pathologies during the COVID-19 pandemic offered better equity in the provision of services in remote locations, a widespread use assessment should be undertaken before this tool is adopted.
ABSTRACT
IntroductionArtificial intelligence (AI) and innovative technology offer opportunities for enhanced health care during the COVID-19 pandemic. Populations living in low-income countries do not have access to reverse transcription polymerase chain reaction (RT-PCR) testing for COVID-19 and, thus, depend on the scarce resources of their health system. In this context, an automated screening system for COVID-19 based on AI for a telemedicine platform could be directed towards alleviating the current lack of trained radiologists who can interpret computed tomography images at countryside hospitals.MethodsThis descriptive study was carried out in Paraguay by the Telemedicine Unit of the Ministry of Public Health and Social Welfare in collaboration with the Department of Biomedical Engineering and Imaging of the Health Science Research Institute and the University of the Basque Country. The utility of the screening system for COVID-19 was analyzed by dividing the results from two tailored AI systems implemented in 14 public hospitals into four likelihood levels for COVID-19.ResultsBetween March and October 2020, 911 COVID-19 diagnoses were performed in 14 regional hospitals (62.6% were men and 37.4% were women). The average age of the patients diagnosed with COVID-19 was 50.7 years;59.1% were aged 19 to 59 years. The two AI systems used have different background information for detecting COVID-19. The most common findings were severe pneumonia and bilateral pneumonia with pleural effusions. The role of computed tomography was to find lesions and evaluate the effects of treatment. The sensitivity of AI for detecting COVID-19 was 93%.ConclusionsAI technology could help in developing a screening system for COVID-19 and other respiratory pathologies. It could speed up medical imaging diagnosis at regional hospitals for patients with suspected infection during the COVID-19 pandemic and rationalize scarce RT-PCR and specialized human resources in low-income countries. These results must be contextualized with the local or regional epidemiological profile before widespread implementation.
ABSTRACT
Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country's public hospitals. The patients' digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27-59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19-26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.